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Master of Science in Al and Data Science

Introduction

About the Program

Master the Future of Intelligence and Innovation
In an era defined by rapid digital transformation, the ability to harness the power of intelligent machines. and vast amounts of data has become one of the most valuable skillsets of the 21st century. The Master of Science in Artificial Intelligence and Data Science (MSAIDS) at AUE is not just a postgraduate degree it’s a launchpad for visionary thinkers, problem-solvers, and change-makers who are ready to lead the next wave of technological evolution.

Artificial Intelligence and Data Science are at the core of today’s most groundbreaking advancements-from predictive healthcare and autonomous vehicles to smart cities, financial forecasting, and intelligent robotics. Our MSAIDS program is designed to prepare students for the profound impact. these technologies are having and will continue to have on society, industries, and human potential.

This program offers more than technical mastery. It is a transformative academic journey that combines. rigorous scientific foundations, hands-on experience, and ethical foresight. Students will engage with complex datasets, develop intelligent systems, master machine learning and deep learning techniques, and explore the legal, societal, and ethical dimensions of Al in a globalized world.

What Makes Our Program Unique?
At AUE, we don’t just teach Al and Data Science we cultivate innovators who will define its next frontier. Here’s what sets our program apart:

    • Cutting-Edge Curriculum: A strategically designed sequence of courses that covers data engineering, machine learning, deep learning, ethics, research methodologies, and specialized topics in computer vision, NLP, decision systems, and intelligent agents.
    • Hands-On Thesis Experience: Our two-stage thesis journey enables students to dive into real-world research and contribute original work to the field under expert supervision.
    • Future-Ready Specializations: With electives in cloud-based Al, robotics, digital image. processing, and cognitive systems, students gain practical skills aligned with industry’s evolving needs.
    • Global Perspective, Local Relevance: We prepare students to address global challenges while. responding to UAE and regional demands for Al innovation, ethical governance, and data-driven decision-making.
    • Expert Faculty & Research Culture: Learn from faculty actively engaged in groundbreaking research and supported by a thriving academic ecosystem that values innovation and collaboration.

Who Should Join This Program?
This program is ideal for professionals and graduates with a background in computing, engineering, mathematics, or related fields who want to become leaders in Al, machine learning, and data science. Whether you aspire to be a data scientist, Al engineer, or applied researcher, this is your launchpad.

Program Mission
To develop highly skilled Al and data science professionals capable of designing intelligent systems, analyzing complex datasets, and making ethical, data-driven decisions that transform industries, drive innovation, and serve society.

Program at a glance


Total Credit Hours

30 Credit Hours

Duration

2 Years

Instruction Language

English

Quality assured

Accreditations

Accredited by CAA
Prof. Faruq Al-Omari

Program Director

Prof. Faruq Al-Omari

Professor - Dean / Acting Program Director of the Master of Science in AI and Data Science

Credit breakdown

Program Structure

Course CategoryTotal Number of CoursesTotal Number of Credit Hours
Core Courses618
Elective Courses26
Thesis16
Total930 Credit Hours

Curriculum

Courses & Descriptions

MAI 622 Advanced Deep Learning Technologies 3 CH
MAI 602 Big Data Analytics and Mining 3 CH
MAI 601 Data Preparation and Statistical Analysis 3 CH
MAI 611 Ethics and Governance in Artificial Intelligence 3 CH
MAI 621 Machine Learning Foundations and Applications 3 CH
RMT 600 Research Methods 3 CH
MAI 700 Thesis 6 CH
MAI 642 Advanced Natural Language Processing and Transformers 3 CH
MAI 650 Cloud Computing for Data-Driven AI Applications 3 CH
MAI 653 Cognitive and Autonomous Robotics 3 CH
MAI 641 Digital Image Processing 3 CH
MAI 652 Intelligent and Autonomous Agents 3 CH
MAI 651 Intelligent Decision Support Systems 3 CH

Semester by semester

Study Plan

2
Credit hours
30
Duration
2 years
Terms
4
  • Core 18 CH · 60%
  • Electives 6 CH · 20%
  • Thesis 6 CH · 20%

Swipe to see each year →

  1. Year 118 CH

    Semester 19 CH

    • MAI 601 3 CH Data Preparation and Statistical Analysis Core Courses
    • MAI 611 3 CH Ethics and Governance in Artificial Intelligence Core Courses
    • MAI 621 3 CH Machine Learning Foundations and Applications Core Courses

    Semester 29 CH

    • MAI 602 3 CH Big Data Analytics and Mining Core Courses
    • RMT 600 3 CH Research Methods Core Courses
    • — 3 CH Advanced Deep Learning Techniques Core Courses
  2. Year 212 CH

    Semester 36 CH

    • — 3 CH Elective Elective 1
    • — 3 CH Elective Elective 2

    Semester 46 CH

    • MAI 700 6 CH Thesis
Study Plan - Master of Science in Al and Data Science

What we aim for

Program Goals

Program Goals
  1. Equip students with specialized knowledge and skills to solve real-world challenges.
  2. Instill ethical practices and promote responsible Al.
  3. Foster lifelong learning and adaptability to evolving technologies.
  4. Develop leadership and interdisciplinary collaboration skills.
  5. Promote innovation and research for impactful solutions.

What you will learn

Program Learning Outcomes

Program Learning Outcomes
  1. Identify, analyze, and define complex Al and Data Science problems, applying advanced principles of computing, mathematics, and domain-specific knowledge to develop effective and innovative solutions.
  2. Design, develop, and evaluate sophisticated Al and Data Science models, algorithms, and systems that address real-world challenges, considering factors such as scalability, performance, and constraints.
  3. Apply advanced mathematical, statistical, and computational methods to model data, derive insights, and create state-of-the-art machine learning and Al-based solutions.
  4. Demonstrate an understanding of ethical, legal, and societal implications of Al and Data Science, making informed decisions that promote fairness, transparency, and responsible use of data.
  5. Develop leadership skills to lead interdisciplinary teams effectively, communicate technical information and data driven insights clearly, and collaborate efficiently in Al and Data Science projects.
  6. Continuously acquire and apply new knowledge in Al and Data Science, adapting to evolving technologies, methodologies, and industry standards with an emphasis on research and innovation.

Requirements

Program Requirements

Graduation Requirements

For graduate degree completion, graduate students must satisfy the following requirements:

  • Earn a minimum CGPA of 3.00 on a scale of 4.00.
  • Successfully complete all courses as described in the study plan.
  • The Degree Completion requirements must be met within the timeframe of the program.
  • Successfully complete the three “Thesis” courses.

Admission Requirements

Please Click here to know more information

When can I join?

Three intakes every year

Students can join at the beginning of any semester.

Intake 01

September

Fall Semester


Intake 02

January

Spring Semester


Intake 03

May

Summer Semester


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